Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Poonia, Ramesh Chandra; Rathore, Vishal Singh; Kumar, Ajay Title A name given to the resource A Novel Machine Learning Ensemble Approach for Corrupt Data Packet Identification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1749 LNNS;pp.314-325 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-13003-7_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-13003-7_30</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028291127?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028291127?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Poonia R.C., Department of Computer Science, CHIRST University, Karnataka, Bangalore, India; Rathore V.S., Department of Computer Science and Engineering, JECRC University, Rajasthan, Jaipur, India; Kumar A., Department of Computer Science and Engineering, JECRC University, Rajasthan, Jaipur, India Description An account of the resource In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Corrupt Data Packet Detection; Ensemble Learning; Security; Stacking Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303213002-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online